How to crop image with nonlinear cropping shape?
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Ivan Shorokhov
am 16 Feb. 2015
Kommentiert: Image Analyst
am 16 Feb. 2015
Dear all,
I have following image:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/147156/image.jpeg)
And would like to crop it as follows:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/147157/image.jpeg)
Here is the code I'm using right now is:
clc;close all;clear all;
corn=imread('After_phi_o-phi-f.jpg');
mm_corn=imerode(corn,strel('disk',1));
bw_corn=im2bw(mm_corn, graythresh(mm_corn));
cc_corn = bwconncomp(bw_corn);
aba_corn = [cellfun(@numel,cc_corn.PixelIdxList)];
[mv_corn,ind] = sort(aba_corn,'descend');
L_corn=labelmatrix(cc_corn);
ki_corn = find(aba_corn >= mv_corn(2));
mbi_corn = ismember(L_corn, ki_corn);
bw_corn(~mbi_corn) = 0;
Ibw = imfill(bw_corn,'holes');
Ilabel = bwlabel(Ibw);
stat = regionprops(Ilabel,'centroid');
imshow(bwconvhull(im2bw(corn, graythresh(corn)))); hold on;
plot([stat(1).Centroid(1),stat(2).Centroid(1)], [stat(1).Centroid(2),stat(2).Centroid(2)], 'r');1)
And result is:
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/147158/image.jpeg)
So I want cut information from the left until the red line, how I can do it?
Thanks for any help.
4 Kommentare
Akzeptierte Antwort
Image Analyst
am 16 Feb. 2015
Get the horizontal and vertical profiles using sum(), and get the top and bottom row and left and right column using find()
horizontalProfile = sum(binaryImage, 1);
verticalProfile = sum(binaryImage, 2);
topRow = find(verticalProfile, 1, 'first');
bottomRow = find(verticalProfile, 1, 'last');
leftColumn= find(horizontalProfile, 1, 'first');
rightColumn = find(horizontalProfile, 1, 'last');
croppedImage = binaryImage(topRow:bottomRow, leftColumn:rightColumn);
2 Kommentare
Image Analyst
am 16 Feb. 2015
Is the line always red? And does it never extend to the boundaries of the blob? If so I'd suggest you extract the red line, then find the endpoints of it with bwmorph(), then draw a black line in the image using imline(), demo attached. Then extract the biggest blob, demo attached.
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